U.S. patent application number 16/180351 was filed with the patent office on 2020-05-07 for intelligent career planning in a computing environment.
This patent application is currently assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION. The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Oznur ALKAN, Adi I. BOTEA, Elizabeth DALY, Akihiro KISHIMOTO, Radu MARINESCU, Christian MUISE.
Application Number | 20200143498 16/180351 |
Document ID | / |
Family ID | 70458827 |
Filed Date | 2020-05-07 |
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United States Patent
Application |
20200143498 |
Kind Code |
A1 |
ALKAN; Oznur ; et
al. |
May 7, 2020 |
INTELLIGENT CAREER PLANNING IN A COMPUTING ENVIRONMENT
Abstract
Embodiments for intelligent career planning actions in a
computing environment by a processor. A career planning model may
be created for a user according to a career goal, a user profile,
and one or more alternative user profiles and historical data of
alternative users having achieved the career goal. A career plan
may be generated for the user according to the career planning
model.
Inventors: |
ALKAN; Oznur; (Clonsilla,
IE) ; BOTEA; Adi I.; (Dublin, IE) ; DALY;
Elizabeth; (Monkstown, IE) ; KISHIMOTO; Akihiro;
(Castleknock, IE) ; MARINESCU; Radu; (Dublin,
IE) ; MUISE; Christian; (Somerville, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Assignee: |
INTERNATIONAL BUSINESS MACHINES
CORPORATION
Armonk
NY
|
Family ID: |
70458827 |
Appl. No.: |
16/180351 |
Filed: |
November 5, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 20/00 20190101;
G06N 7/023 20130101; G06N 5/003 20130101; G06N 3/088 20130101; G06N
7/005 20130101; G06N 5/04 20130101; G06Q 50/2057 20130101; G06N
3/126 20130101; G06N 5/02 20130101; G06N 3/006 20130101; G06N 20/20
20190101 |
International
Class: |
G06Q 50/20 20060101
G06Q050/20; G06N 99/00 20060101 G06N099/00 |
Claims
1. A method, by a processor, for intelligent career planning
actions in a computing environment, comprising: creating a career
planning model for a user according to a career goal, a user
profile, and one or more alternative user profiles and historical
data of alternative users having achieved the career goal;
generating a career plan for the user according to the career
planning model.
2. The method of claim 1, further including identifying a series of
actions steps for achieving the career goal.
3. The method of claim 1, further including identifying a subset of
actions for having a negative impact on achieving the career
goal.
4. The method of claim 1, further including inferring one or more
actions steps for completing a series of actions for achieving the
career goal.
5. The method of claim 1, further including synthesizing the career
planning model from a series of actions steps for achieving the
career goal.
6. The method of claim 1, further including identifying positive or
negative impacts for achieving the career goal according to
alternative results generated from selecting alternative choices at
each actions step performed by the alternative users to achieve the
career goal.
7. The method of claim 1, further including: identifying a
plurality of valid career path trajectories having a series of
actions steps for achieving the career goal; and selecting one or
more of the plurality of valid career path trajectories having
greater positive impact upon the user for achieving the career goal
as compared to alternative ones of the plurality of valid career
path trajectories.
8. A system for intelligent career planning actions in a computing
environment, comprising: one or more computers with executable
instructions that when executed cause the system to: create a
career planning model for a user according to a career goal, a user
profile, and one or more alternative user profiles and historical
data of alternative users having achieved the career goal; and
generate a career plan for the user according to the career
planning model.
9. The system of claim 8, wherein the executable instructions
further identify a series of actions steps for achieving the career
goal.
10. The system of claim 8, wherein the executable instructions
further identify a subset of actions for having a negative impact
on achieving the career goal.
11. The system of claim 8, wherein the executable instructions
further infer one or more actions steps for completing a series of
actions for achieving the career goal.
12. The system of claim 8, wherein the executable instructions
further synthesize the career planning model from a series of
actions steps for achieving the career goal.
13. The system of claim 8, wherein the executable instructions
further identify positive or negative impacts for achieving the
career goal according to alternative results generated from
selecting alternative choices at each actions step performed by the
alternative users to achieve the career goal.
14. The system of claim 8, wherein the executable instructions
further: identify a plurality of valid career path trajectories
having a series of actions steps for achieving the career goal; and
select one or more of the plurality of valid career path
trajectories having greater positive impact upon the user for
achieving the career goal as compared to alternative ones of the
plurality of valid career path trajectories.
15. A computer program product for intelligent career planning
actions by a processor, the computer program product comprising a
non-transitory computer-readable storage medium having
computer-readable program code portions stored therein, the
computer-readable program code portions comprising: an executable
portion that creates a career planning model for a user according
to a career goal, a user profile, and one or more alternative user
profiles and historical data of alternative users having achieved
the career goal; and an executable portion that generates a career
plan for the user according to the career planning model.
16. The computer program product of claim 15, further including an
executable portion that: identifies a series of actions steps for
achieving the career goal; and identify a subset of actions for
having a negative impact on achieving the career goal.
17. The computer program product of claim 15, further including an
executable portion that infers one or more actions steps for
completing a series of actions for achieving the career goal.
18. The computer program product of claim 15, further including an
executable portion that synthesizes the career planning model from
a series of actions steps for achieving the career goal.
19. The computer program product of claim 15, further including an
executable portion that identifies positive or negative impacts for
achieving the career goal according to alternative results
generated from selecting alternative choices at each actions step
performed by the alternative users to achieve the career goal.
20. The computer program product of claim 15, further including an
executable portion that: identifies a plurality of valid career
path trajectories having a series of actions steps for achieving
the career goal; and selects one or more of the plurality of valid
career path trajectories having greater positive impact upon the
user for achieving the career goal as compared to alternative ones
of the plurality of valid career path trajectories.
Description
BACKGROUND OF THE INVENTION
Field of the Invention
[0001] The present invention relates in general to computing
systems, and more particularly, to various embodiments for
intelligent career planning actions in a computing environment
using a computing processor.
Description of the Related Art
[0002] In today's society, consumers, business persons, educators,
and others use various computing network systems with increasing
frequency in a variety of settings' The advent of computers and
networking technologies have made possible the increase in the
quality of life while enhancing day-to-day activities. Current
network and communications technologies, such as machine-to-machine
(M2M) technologies and the Internet, allow devices to communicate
and interact more directly with each other and even monitor
activities of daily living ("ADL"), which can be used to improve
the quality of life and future life choices and planning.
SUMMARY OF THE INVENTION
[0003] Various embodiments for intelligent career planning actions
in a computing environment by a processor, are provided. In one
embodiment, by way of example only, a method for implementing
intelligent career planning actions in a computing environment,
again by a processor, is provided. A career planning model may be
created for a user according to a career goal, a user profile, and
one or more alternative user profiles and historical data of
alternative users having achieved the career goal. A career plan
may be generated for the user according to the career planning
model.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] In order that the advantages of the invention will be
readily understood, a more particular description of the invention
briefly described above will be rendered by reference to specific
embodiments that are illustrated in the appended drawings.
Understanding that these drawings depict only typical embodiments
of the invention and are not therefore to be considered to be
limiting of its scope, the invention will be described and
explained with additional specificity and detail through the use of
the accompanying drawings, in which:
[0005] FIG. 1 is a block diagram depicting an exemplary cloud
computing node according to an embodiment of the present
invention;
[0006] FIG. 2 is an additional block diagram depicting an exemplary
cloud computing environment according to an embodiment of the
present invention;
[0007] FIG. 3 is an additional block diagram depicting abstraction
model layers according to an embodiment of the present
invention;
[0008] FIG. 4 is an additional block diagram depicting an exemplary
functional relationship between various aspects of the present
invention;
[0009] FIG. 5 is a block-flow diagram depicting an exemplary
operation for intelligent career planning actions in a computing
environment by a processor in which aspects of the present
invention may be realized;
[0010] FIG. 6 is a diagram depicting an additional model-based
prediction for intelligent career planning actions by a processor,
again in which aspects of the present invention may be realized;
and
[0011] FIG. 7 is a flowchart diagram depicting an exemplary method
for implementing intelligent career planning actions in computing
environment in which aspects of the present invention may be
realized.
DETAILED DESCRIPTION OF THE DRAWINGS
[0012] As the amount of electronic information continues to
increase, the demand for sophisticated information access systems
also grows. Digital or "online" data has become increasingly
accessible through real-time, global computer networks. The data
may reflect many aspects of various organizations and groups or
individuals, including scientific, political, governmental,
educational, businesses, and so forth.
[0013] Moreover, people-driven organizations tend to rely on an
employee-centric organizational structure. Employee skills and
performances are directly or indirectly encoded in many different
information sources ranging from their curriculum vitae ("CVs") to
skill-sets, performance evaluations, and/or projects associated
with them within the organization. As a result of this, a full
understanding of an employee's skill-set and performance evaluation
is critical for many companies. That said, with the vast amount of
educational and occupational opportunities, making and selecting
correct educational and career choices for an individual plays a
significant impact on an individual becoming a qualified
candidate/employee for a particular organization.
[0014] However, selecting the correct career trajectory that
maximizes the greatest likelihood to accomplish a career goal is
particularly difficult due to a lack of collective information and
limited access to many resources. As such, an individual may spend
countless hours researching a career path only to find that such a
path lacks many action steps or sub-steps required for
accomplishing the career goal. Thus, a need exists for intelligent
career planning actions in a computing environment using a
computing processor.
[0015] Accordingly, the present invention provides for a cognitive
system that provides for intelligent career planning actions. In
one aspect, a career planning model may be created for a user
according to a career goal, a user profile, and one or more
alternative user profiles and historical data of alternative users
having achieved the career goal. A career plan may be generated for
the user according to the career planning model. Thus, the present
invention provides for intelligent career planning having one or
more actions to achieve a goal using the experience of others who
have achieved a similar goal, starting from a state similar of the
current state of the user. A synthesized plan and other identified
factors can be identified and provided to a user via a graphical
user interface ("GUI") of a computing device (e.g., an Internet of
Things "IoT" device, computer, etc.). In an additional aspect, the
created career plan may be further refined for a stronger fit with
the current user. For example, the user can review the recommended
plan and provide additional feedback to more accurately fine tune
the plan such as, for example, the user may say "I don't like job
roles that involve frequent travel". The plan may use the feedback
to refine the solution so that roles that involve frequent travel
are avoided.
[0016] In an additional aspect, cognitive or "cognition" may refer
to a mental action or process of acquiring knowledge and
understanding through thought, experience, and one or more senses
using machine learning (which may include using sensor based
devices or other computing systems that include audio or video
devices). Cognitive may also refer to identifying patterns of
behavior, leading to a "learning" of one or more events,
operations, or processes. The term "cognitive" or "cognition" may
refer to a cognitive system. The cognitive system may be a
specialized computer system, or set of computer systems, configured
with hardware and/or software logic (in combination with hardware
logic upon which the software executes) to emulate human cognitive
functions. These cognitive systems apply human-like characteristics
to convey and manipulate ideas which, when combined with the
inherent strengths of digital computing, can solve problems with a
high degree of accuracy (e.g., within a defined percentage range or
above an accuracy threshold) and resilience on a large scale. A
cognitive system may perform one or more computer-implemented
cognitive operations that approximate a human thought process while
enabling a user or a computing system to interact in a more natural
manner. A cognitive system may comprise artificial intelligence
logic, such as natural language processing (NLP) based logic, for
example, and machine learning logic, which may be provided as
specialized hardware, software executed on hardware, or any
combination of specialized hardware and software executed on
hardware. The logic of the cognitive system may implement the
cognitive operation(s), examples of which include, but are not
limited to, question answering, identification of related concepts
within different portions of content in a corpus, and intelligent
search algorithms, such as Internet web page searches.
[0017] In general, such cognitive systems are able to perform the
following functions: 1) Navigate the complexities of human language
and understanding; 2) Ingest and process vast amounts of structured
and unstructured data; 3) Generate and evaluate hypotheses; 4)
Weigh and evaluate responses that are based only on relevant
evidence; 5) Provide situation-specific advice, insights,
estimations, determinations, evaluations, calculations, and
guidance; 6) Improve knowledge and learn with each iteration and
interaction through machine learning processes; 7) Enable decision
making at the point of impact (contextual guidance); 8) Scale in
proportion to a task, process, or operation; 9) Extend and magnify
human expertise and cognition; 10) Identify resonating, human-like
attributes and traits from natural language; 11) Deduce various
language specific or agnostic attributes from natural language; 12)
Memorize and recall relevant data points (images, text, voice)
(e.g., a high degree of relevant recollection from data points
(images, text, voice) (memorization and recall)); and/or 13)
Predict and sense with situational awareness operations that mimic
human cognition based on experiences.
[0018] It should be noted that one or more calculations may be
performed using various mathematical operations or functions that
may involve one or more mathematical operations (e.g., solving
differential equations or partial differential equations
analytically or computationally, using addition, subtraction,
division, multiplication, standard deviations, means, averages,
percentages, statistical modeling using statistical distributions,
by finding minimums, maximums or similar thresholds for combined
variables, etc.).
[0019] It is understood in advance that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0020] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0021] Characteristics are as follows:
[0022] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0023] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0024] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0025] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0026] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0027] Service Models are as follows:
[0028] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0029] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0030] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0031] Deployment Models are as follows:
[0032] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0033] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0034] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0035] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities, but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0036] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0037] Referring now to FIG. 1, a schematic of an example of a
cloud computing node is shown. Cloud computing node 10 is only one
example of a suitable cloud computing node and is not intended to
suggest any limitation as to the scope of use or functionality of
embodiments of the invention described herein. Regardless, cloud
computing node 10 is capable of being implemented and/or performing
any of the functionality set forth hereinabove.
[0038] In cloud computing node 10 there is a computer system/server
12, which is operational with numerous other general purpose or
special purpose computing system environments or configurations.
Examples of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer
system/server 12 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
hand-held or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
[0039] Computer system/server 12 may be described in the general
context of computer system-executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
[0040] As shown in FIG. 1, computer system/server 12 in cloud
computing node 10 is shown in the form of a general-purpose
computing device. The components of computer system/server 12 may
include, but are not limited to, one or more processors or
processing units 16 (which may be referred to herein individually
and/or collectively as "processor"), a system memory 28, and a bus
18 that couples various system components including system memory
28 to processor 16.
[0041] Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
[0042] Computer system/server 12 typically includes a variety of
computer system readable media. Such media may be any available
media that is accessible by computer system/server 12, and it
includes both volatile and non-volatile media, removable and
non-removable media.
[0043] System memory 28 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
30 and/or cache memory 32.
[0044] Computer system/server 12 may further include other
removable/non-removable, volatile/non-volatile computer system
storage media. By way of example only, storage system 34 can be
provided for reading from and writing to a non-removable,
non-volatile magnetic media (not shown and typically called a "hard
drive"). Although not shown, a magnetic disk drive for reading from
and writing to a removable, non-volatile magnetic disk (e.g., a
"floppy disk"), and an optical disk drive for reading from or
writing to a removable, non-volatile optical disk such as a CD-ROM,
DVD-ROM or other optical media can be provided. In such instances,
each can be connected to bus 18 by one or more data media
interfaces. As will be further depicted and described below, memory
28 may include at least one program product having a set (e.g., at
least one) of program modules that are configured to carry out the
functions of embodiments of the invention.
[0045] Program/utility 40, having a set (at least one) of program
modules 42, may be stored in memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 42
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein.
[0046] Computer system/server 12 may also communicate with one or
more external devices 14 such as a keyboard, a pointing device, a
display 24, etc.; one or more devices that enable a user to
interact with computer system/server 12; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system/server 12 to
communicate with one or more other computing devices. Such
communication can occur via Input/output (I/O) interfaces 22. Still
yet, computer system/server 12 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 20. As depicted, network adapter 20 communicates
with the other components of computer system/server 12 via bus 18.
It should be understood that although not shown, other hardware
and/or software components could be used in conjunction with
computer system/server 12. Examples, include, but are not limited
to: microcode, device drivers, redundant processing units, external
disk drive arrays, RAID systems, tape drives, and data archival
storage systems, etc.
[0047] Referring now to FIG. 2, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 comprises one or more cloud computing nodes 10 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 10 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing device. It
is understood that the types of computing devices 54A-N shown in
FIG. 2 are intended to be illustrative only and that computing
nodes 10 and cloud computing environment 50 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0048] Referring now to FIG. 3, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 2) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 3 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0049] Device layer 55 includes physical and/or virtual devices,
embedded with and/or standalone electronics, sensors, actuators,
and other objects to perform various tasks in a cloud computing
environment 50. Each of the devices in the device layer 55
incorporates networking capability to other functional abstraction
layers such that information obtained from the devices may be
provided thereto, and/or information from the other abstraction
layers may be provided to the devices. In one embodiment, the
various devices inclusive of the device layer 55 may incorporate a
network of entities collectively known as the "internet of things"
(IoT). Such a network of entities allows for intercommunication,
collection, and dissemination of data to accomplish a great variety
of purposes, as one of ordinary skill in the art will
appreciate.
[0050] Device layer 55 as shown includes sensor 52, actuator 53,
"learning" thermostat 56 with integrated processing, sensor, and
networking electronics, camera 57, controllable household
outlet/receptacle 58, and controllable electrical switch 59 as
shown. Other possible devices may include, but are not limited to
various additional sensor devices, networking devices, electronics
devices (such as a remote-control device), additional actuator
devices, so called "smart" appliances such as a refrigerator or
washer/dryer, and a wide variety of other possible interconnected
objects.
[0051] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0052] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0053] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 82 provides cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may comprise application software
licenses. Security provides identity verification for cloud
consumers and tasks, as well as protection for data and other
resources. User portal 83 provides access to the cloud computing
environment for consumers and system administrators. Service level
management 84 provides cloud computing resource allocation and
management such that required service levels are met. Service Level
Agreement (SLA) planning and fulfillment 85 provides
pre-arrangement for, and procurement of, cloud computing resources
for which a future requirement is anticipated in accordance with an
SLA.
[0054] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and, in
the context of the illustrated embodiments of the present
invention, various workloads and functions 96 for intelligent
career planning actions in a computing environment. In addition,
workloads and functions 96 for intelligent career planning actions
in a computing environment may include such operations as data
analysis, machine learning (e.g., artificial intelligence, natural
language processing, etc.), user analysis, IoT sensor device
detections, operation and/or analysis, as will be further
described. One of ordinary skill in the art will appreciate that
the workloads and functions 96 for intelligent career planning
actions in a computing environment may also work in conjunction
with other portions of the various abstraction layers, such as
those in hardware and software 60, virtualization 70, management
80, and other workloads 90 (such as data analytics processing 94,
for example) to accomplish the various purposes of the illustrated
embodiments of the present invention.
[0055] Turning now to FIG. 4, a block diagram depicting exemplary
functional components 400 according to various mechanisms of the
illustrated embodiments is shown. In one aspect, one or more of the
components, modules, services, applications, and/or functions
described in FIGS. 1-3 may be used in FIG. 4. An intelligent career
planning service 410 is shown, incorporating processing unit
("processor") 420 to perform various computational, data processing
and other functionality in accordance with various aspects of the
present invention. The intelligent career planning service 410 may
be provided by the computer system/server 12 of FIG. 1. The
processing unit 420 may be in communication with memory 430. The
intelligent career planning service 410 may also include a user
profile component 440, a career planning model component 450, a
database 460 (e.g., knowledge domain), and a machine learning model
component 470.
[0056] As one of ordinary skill in the art will appreciate, the
depiction of the various functional units in the intelligent career
planning service 410 is for purposes of illustration, as the
functional units may be located within the intelligent career
planning service 410 or elsewhere within and/or between distributed
computing components.
[0057] In one aspect, the computer system/server 12 and/or the
intelligent career planning service 410 may provide virtualized
computing services (i.e., virtualized computing, virtualized
storage, virtualized networking, etc.). More specifically, the
intelligent career planning service 410 may provide, and/or be
included in, a virtualized computing, virtualized storage,
virtualized networking and other virtualized services that are
executing on a hardware substrate.
[0058] The user profile component 440 may collect, store, maintain,
and/or update one or more user profiles. For example, an
organization may store and maintain a user profile for each
particular entity/person (e.g., employee, student, group member,
etc.). In an additional aspect, the user profile component 440 may
store a user profile for a selected user according to a specified
career goal. Historical data of alternative users having achieved
the specified career goal may also be stored and maintained via
database 460 by the user profile component.
[0059] In one aspect, career planning model component 450 may
create a career planning model for a user according to a career
goal, a user profile, and one or more alternative user profiles and
historical data of alternative users having achieved the career
goal. The career planning model component 450 may generate a career
plan for the user according to the career planning model. As an
additional aspect, the career planning model component 450 may
identify a series of actions steps for achieving the career goal,
identify a subset of actions for having a negative impact on
achieving the career goal, and/or infer one or more actions steps
for completing the series of actions steps for achieving the career
goal. The career planning model component 450 may also synthesize
the career planning model from a series of actions steps for
achieving the career goal.
[0060] Using the machine learning component 470 and the career
planning model component 450, positive and/or negative impacts for
achieving the career goal may also be identified according to
alternative results generated from selecting alternative choices at
each actions step performed by the alternative users to achieve the
career goal.
[0061] The career planning model component 450, in association with
the machine learning component 470, may identify a plurality of
valid career path trajectories having a series of actions steps for
achieving the career goal, and/or may select one or more of the
plurality of valid career path trajectories having a greatest
positive impact upon the user for achieving the career goal as
compared to alternative ones of the plurality of valid career path
trajectories.
[0062] In one embodiment, by way of example only, the machine
learning component 470 as used herein may include, for example, an
instance of IBM.RTM. Watson.RTM. such as Watson.RTM. Analytics
(IBM.RTM. and Watson.RTM. are trademarks of International Business
Machines Corporation). By way of example only, the machine learning
component 470 may determine one or more heuristics and machine
learning based models using a wide variety of combinations of
methods, such as supervised learning, unsupervised learning,
temporal difference learning, reinforcement learning and so forth.
Some non-limiting examples of supervised learning which may be used
with the present technology include AODE (averaged one-dependence
estimators), artificial neural networks, Bayesian statistics, naive
Bayes classifier, Bayesian network, case-based reasoning, decision
trees, inductive logic programming, Gaussian process regression,
gene expression programming, group method of data handling (GMDH),
learning automata, learning vector quantization, minimum message
length (decision trees, decision graphs, etc.), lazy learning,
instance-based learning, nearest neighbor algorithm, analogical
modeling, probably approximately correct (PAC) learning, ripple
down rules, a knowledge acquisition methodology, symbolic machine
learning algorithms, sub symbolic machine learning algorithms,
support vector machines, random forests, ensembles of classifiers,
bootstrap aggregating (bagging), boosting (meta-algorithm), ordinal
classification, regression analysis, information fuzzy networks
(IFN), statistical classification, linear classifiers, fisher's
linear discriminant, logistic regression, perceptron, support
vector machines, quadratic classifiers, k-nearest neighbor, hidden
Markov models and boosting. Some non-limiting examples of
unsupervised learning which may be used with the present technology
include artificial neural network, data clustering,
expectation-maximization, self-organizing map, radial basis
function network, vector quantization, generative topographic map,
information bottleneck method, IBSEAD (distributed autonomous
entity systems based interaction), association rule learning,
apriori algorithm, eclat algorithm, FP-growth algorithm,
hierarchical clustering, single-linkage clustering, conceptual
clustering, partitional clustering, k-means algorithm, fuzzy
clustering, and reinforcement learning. Some non-limiting examples
of temporal difference learning may include Q-learning and learning
automata. Specific details regarding any of the examples of
supervised, unsupervised, temporal difference or other machine
learning described in this paragraph are known and are considered
to be within the scope of this disclosure.
[0063] In one aspect, the database 460 may be a knowledge domain
and/or an ontology of concepts representing a domain of knowledge
(e.g., skills, tasks, academic/educational requirements, action
steps, pathways, etc.) for planning a career pathway and providing
one or more steps and/or sub steps for recommending one or more
actions to achieve the career goal.
[0064] A thesaurus or ontology may be used as the domain knowledge
of the database 460 and may also be used to identify relationships
between observed and/or unobserved variables parameters. In one
aspect, the term "domain" is a term intended to have its ordinary
meaning. In addition, the term "domain" may include an area of
expertise for a system or a collection of material, information,
content and/or other resources related to a particular subject or
subjects. For example, a domain can refer to environmental,
scientific, industrial, educational, statistical data, commercial,
health, manufacturer information, technological information, one or
more decisions and response types in a variety of applications. A
domain can refer to information related to any particular subject
matter or a combination of selected subjects.
[0065] Turning now to FIG. 5, a block diagram of exemplary
functionality 500 relating to intelligent career planning actions
in a computing environment. As shown, the various blocks of
functionality are depicted with arrows designating the blocks' 500
relationships with each other and to show process flow.
Additionally, descriptive information is also seen relating each of
the functional blocks 500. As will be seen, many of the functional
blocks may also be considered "modules" of functionality, in the
same descriptive sense as has been previously described in FIGS.
1-4. With the foregoing in mind, the module blocks 500 may also be
incorporated into various hardware and software components of a
system for image enhancement in accordance with the present
invention. Many of the functional blocks 500 may execute as
background processes on various components, either in distributed
computing components, or on the user device, or elsewhere.
[0066] Starting with block 502, a user may input and/or define a
career goal. Simultaneously and/or in parallel with block 502 (or
prior to or following block 502), a user profile and/or a
collection of user profiles of other users with historical
information (e.g., employee historical data from database) may
collected from database 504 and used as input to build a career
planning model from the profiles of alternative user profiles, as
in block 506. The collected user profiles may be those
persons/employees identified as having accomplished the career goal
of a selected user and/or other users that have accomplished a
career goal from other career/employment trajectories.
Additionally, other user profiles having failed to accomplish the
career goal of and/or other users that have failed to accomplish a
career goal from other career/employment trajectories such as, for
example, a user being fired/terminated from employment may be
collected, identified, and/or analyzed.
[0067] Using the career planning model from block 506 and the
career goal of block 502, one or more career pathway for the
selected user may be planned (e.g., the career pathway/policies are
computed), as in block 508. One or more career pathway plans (e.g.,
policies) may be synthesized to the user, as in block 510. One or
more actions that are likely to achieve the career goals may be
actions, as in block 512. That is, those actions that have a
greatest positive impact upon the user for achieving the career
goal as compared to alternative actions. Said differently, those
actions that exceed an assigned threshold and/or percentage may be
recommended. The recommended actions may be provided as output to a
user, as in block 514. For example, the recommended actions may be
provided to one or more IoT computing devices (e.g., a graphical
user interface of a computer or smart phone).
[0068] In view of the foregoing features and functionalities FIGS.
1-5, consider the following operations for intelligent career
planning actions.
Model-Based Prediction (Career Planning Model)
[0069] For determining one or more action steps and/or action
sub-steps for a career path for a selected user, an action model
(e.g., career planning model of FIG. 5) may be used that describes
each of the prerequisites and effects of action steps and/or action
sub-steps. In one aspect, the action model (e.g., career planning
model) may be an abstraction for each of the action steps and/or
action sub-steps in a career path for each user, and each
employee's history for achieving a career coal may be viewed as an
individual plan for that particular user. To construct a policy of
what a selected user (e.g., an existing employee) should do (e.g.,
what action steps and/or action sub-steps should be performed by
the selected user), each of the identified individual plans of
other users/employees may be generalized as previous "plans" using
a logical regression operation which indicates precisely what
conditions, requirements, operations, or parameters must be met,
satisfied, and/or performed for a plan fragment to achieve the
career objectives of the selected user. That is, actions (e.g.,
"steps") are the building blocks of a plan and a plan fragment may
contain one or more actions/steps. The career pathway policy of
multiple plans can be combined (e.g., synthesized) into a single
"executable" plan capable of making case-specific
recommendations.
Changing Expectations
[0070] An optimized or "best" career path for an employee may
change over time, and previous career planning trajectories may be
invalid and/or not no longer applicable as various conditions,
requirements, operations, parameters, action steps and/or action
sub-steps may have changed or improved. Thus, each career plan
trajectory may be analyzed and determined to be valid and/or
invalid by comparing the career plan trajectory to an
updated/modernized career plan trajectory model of a domain, which
may be performed by ascertaining plan validity of the trajectory.
For example, the ascertaining plan validity of the trajectory
involves monitoring the progress of user along the career and
decide whether the progress is consistent with the plan. For
example, if the plan indicates to stay in a role "A" for 2 years,
but the user has been in the role for 2.5 years, then an alarm may
be raised. Another example is when a role included in a future part
of a plan but no longer exists in the job market (and/or company)
(e.g., a company sells away its hardware division) the roles
specific to that division would no longer exist in the company).
Even upon determining a career plan trajectory is valid, the career
plan trajectory may not be optimal. Thus, one or more career plan
trajectories may be identified for improvement or modernization
according via a comparison operation with the updated/modernized
career plan trajectory model of career planning using techniques
for plan refinement.
Career Path Model Synthesis
[0071] A complete action model (e.g., career path model) with each
action step and/or action sub-step that are required and/or
identified in a career path may not be readily available. Thus, to
create the complete action model, one or more missing action steps
and/or action sub-steps may be inferred.
[0072] In one aspect, when each action steps and/or action
sub-steps in a career path are given as parameterized action
descriptions (e.g., "complete course Java-101"), then existing
domain synthesis operations for planning such as, for example,
learning object-centered models "LOCM" may be applied. If the
career action steps and/or action sub-steps are partially
specified, then one or more inferring operations may be employed to
probabilistically infer one or more remaining action steps and/or
action sub-steps components such as, for example, model-lite
planning) can be employed.
Career Path Antipatterns
[0073] In an additional aspect, just as a career planning goal may
include successful career planning trajectories for a career path,
one or more trajectories may also be provided that represent
negative career trajectories. For example, one or more action steps
and/or action sub-steps that lead to a negative result/failure such
as, for example, being fired/terminated from employment may also be
identifies. Action steps and/or action sub-steps with negative
outcomes (e.g., negative having a negative, failing, or hindering
effect upon achieving the career goal), such as expensive training
that may never be used. Thus, in one aspect, one or more
alternative user career paths (e.g., user career paths of persons
having achieved a career goal) may be matched with known career
paths failures (e.g., persons having failed to achieve the career
goal) may be used to identify and provide one or more indications
(e.g., alerts) to a user that may be engaging in conduct associated
with the known career paths failures (e.g., an employee's behavior
is similar to a previously terminated employee). A knowledge
domain/library of negative career trajectories/paths may be used to
provide targeted feedback for one or alternative uses (e.g.,
managers, supervisors, etc.) to address and provide assistance to
the selected user for improving, correcting, changing one or more
action steps and/or action sub-steps. For example, in a business
setting, one or more action/behavior patterns of failure may be
identified and addressed with the selected user.
[0074] Turning now to FIG. 6, a block diagram 600 of an exemplary
model-based prediction for intelligent career planning actions in a
computing environment. One or more features, functionality,
components, and/or operations of FIGS. 1-5, for providing the
model-based prediction, may be included in FIG. 6.
[0075] FIG. 6 depicts an example of user A (e.g., a young
scientist) at company "Y" having a career goal to become a master
inventor ("MI"). As depicted in FIG. 6, three (3) layers (e.g.,
Level 1, Level, 2, and Level 3) depict each of the various action
steps and/or action sub-steps, conditions, requirements,
operations, or parameters must be met, satisfied, and/or performed
to achieve the career goal (e.g., becoming an MI).
[0076] Thus, the present invention may identify other master
inventors that prior to becoming an MI each were in a similar state
to user A. For example, the present invention may identify various
contextual factors for identifying the similar state such as, for
example, a job role, a level/degree of experience, skills,
educational history, location, training, historical information of
each identified MI, and other various defined parameters. The
cognitive system may analyze the contextual factors/historical
information of the MI's from the identified historical states up to
the time when each identified MI became an MI, and extract actions
performed in each time interval. That is, the present invention
identifies a historical career trajectory of each MI and each
action step and/or action sub-steps executed by each MI in the
process of becoming an MI. In one aspect, for the particular career
goal of "MI" the actions and/or sub-actions can refer to
educational requirements, training, a number of patents filed,
mentoring activities, a number of conferences attended, job
changes, and/or other defined actions/sub-actions. The cognitive
system may synthesize an action plan customized for user A from the
action traces of each of the identified MIs.
[0077] Turning now to FIG. 7, a method 700 for implementing
intelligent career planning actions in a computing environment is
depicted, in which various aspects of the illustrated embodiments
may be implemented. The functionality 700 may be implemented as a
method executed as instructions on a machine, where the
instructions are included on at least one computer readable medium
or on a non-transitory machine-readable storage medium. The
functionality 700 may start in block 702.
[0078] A career planning model may be created for a user according
to a career goal, a user profile, and one or more alternative user
profiles and historical data of alternative users having achieved
the career goal, as in block 704. A career plan may be generated
for the user according to the career planning model, as in block
706. The functionality 700 may end in block 708.
[0079] In one aspect, in conjunction with and/or as part of at
least one block of FIG. 7, the operations of method 700 may include
each of the following. The operations of method 700 may identify a
series of actions steps for achieving the career goal, and identify
a subset of actions for having a negative impact on achieving the
career goal. The operations of method 700 may infer one or more
actions steps for completing a series of actions for achieving the
career goal. The career planning model may be synthesized from a
series of actions steps for achieving the career goal.
[0080] The operations of method 700 may identify positive or
negative impacts for achieving the career goal according to
alternative results generated from selecting alternative choices at
each actions step performed by the alternative users to achieve the
career goal. Additionally, the operations of method 700 may
identify a plurality of valid career path trajectories having a
series of actions steps for achieving the career goal, and select
one or more of the plurality of valid career path trajectories
having greater positive impact upon the user for achieving the
career goal as compared to alternative ones of the plurality of
valid career path trajectories.
[0081] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0082] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0083] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0084] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0085] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions
[0086] These computer readable program instructions may be provided
to a processor of a general-purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0087] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0088] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
* * * * *